Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

Основні поняття

The core message of this article is to enhance the existing hierarchical transformer-based model, UNesT, for whole brain segmentation by integrating the estimation of total intracranial volume (TICV) and posterior fossa volume (PFV) alongside the segmentation of 132 brain regions.

Анотація

The authors propose an enhanced version of the UNesT framework to incorporate intracranial measurements, specifically TICV and PFV, in addition to the segmentation of 132 brain regions.

The approach involves a two-stage process:

1. Pretraining the model using pseudo labels for the 132 brain regions on a large dataset of 4859 T1-weighted MRI scans from multiple sites.
2. Finetuning the pretrained model using 45 T1-weighted MRI scans from the OASIS dataset, where both the 133 whole brain classes (including TICV and PFV) and the corresponding labels are available.

The authors evaluate the performance of their enhanced model using Dice similarity coefficients (DSC) and demonstrate that it can achieve accurate TICV and PFV estimation (DSC of 0.962 and 0.954, respectively) while maintaining a comparable level of performance (DSC of 0.751) on the segmentation of the 132 brain regions compared to the original UNesT model (DSC of 0.759).

The authors also provide a containerized solution using Singularity for end-to-end segmentation, which can accommodate both skull-stripped and non-skull-stripped input data.

Статистика

The authors used 4859 T1-weighted MRI scans from 8 different sites for pretraining, and 45 T1-weighted MRI scans from the OASIS dataset for finetuning.

Цитати

"Our results show that we can achieve accurate TICV/PFV estimation while maintaining a comparable level of performance across 132 regions on whole brain segmentation."

Ключові висновки, отримані з

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

by Xin Yu,Yuche... о arxiv.org 04-12-2024

https://arxiv.org/pdf/2309.04071.pdf

Глибші Запити

How can the integration of TICV and PFV estimation into the whole brain segmentation model be further improved to minimize the trade-off in performance on the 132 brain regions?

To minimize the trade-off in performance on the 132 brain regions while integrating TICV and PFV estimation into the whole brain segmentation model, several strategies can be employed:

Multi-Task Learning: Implement a multi-task learning framework where the model simultaneously optimizes for both the segmentation of the 132 brain regions and the estimation of TICV/PFV. By jointly training the model on these tasks, it can learn to balance the objectives effectively.

Dynamic Weighting: Introduce dynamic weighting mechanisms during training to adjust the importance of the TICV/PFV estimation loss relative to the brain region segmentation loss. This adaptive weighting can help the model prioritize different tasks based on their relative importance at different stages of training.

Architectural Modifications: Explore architectural modifications that allow for better integration of TICV/PFV estimation without compromising the segmentation performance. This could involve designing specialized modules or attention mechanisms that specifically focus on intracranial measurements while maintaining the segmentation quality.

Data Augmentation: Augment the training data with synthetic samples that emphasize TICV/PFV variations to provide the model with a more diverse set of examples for learning. This can help the model generalize better to different TICV/PFV scenarios without sacrificing performance on the brain regions.

Regularization Techniques: Apply regularization techniques such as dropout, batch normalization, or weight decay to prevent overfitting on the TICV/PFV estimation task, which could be causing the trade-off in performance. Regularization can help the model generalize better and improve overall segmentation quality.

By incorporating these strategies, the integration of TICV and PFV estimation into the whole brain segmentation model can be enhanced to minimize the trade-off in performance on the 132 brain regions.

What are the potential clinical applications and implications of having a comprehensive whole brain segmentation model that includes intracranial measurements, such as TICV and PFV?

The integration of intracranial measurements like TICV and PFV into a comprehensive whole brain segmentation model has several significant clinical applications and implications:

Neurodegenerative Disorders: Accurate TICV estimation is crucial for studying neurodegenerative disorders like Alzheimer's disease. Changes in TICV can be indicative of brain atrophy, providing valuable insights into disease progression and treatment monitoring.

Chiari Malformation Diagnosis: PFV estimation plays a vital role in diagnosing conditions like Chiari malformation. By including PFV in the segmentation model, clinicians can better assess patients for this condition and plan appropriate interventions.

Treatment Planning: Comprehensive whole brain segmentation with intracranial measurements can aid in treatment planning for various neurological conditions. Clinicians can use the detailed segmentation to target specific brain regions or assess the impact of interventions on overall brain structure.

Research Studies: Researchers can leverage the detailed segmentation provided by the model to conduct studies on brain morphology, connectivity, and function. The inclusion of TICV and PFV allows for a more holistic analysis of brain structure and its relationship to various cognitive functions.

Personalized Medicine: With accurate TICV and PFV estimation, personalized treatment plans can be developed based on individual brain characteristics. This personalized approach can lead to more effective interventions and better patient outcomes.

Overall, a comprehensive whole brain segmentation model that includes intracranial measurements has the potential to revolutionize clinical practice by providing detailed insights into brain structure and function, facilitating early disease detection, and guiding personalized treatment strategies.

How can the proposed approach be extended to incorporate other clinically relevant brain measurements or features beyond TICV and PFV to enhance the utility of the whole brain segmentation model in various research and clinical settings?

To extend the proposed approach and incorporate additional clinically relevant brain measurements or features beyond TICV and PFV, the following steps can be taken:

Feature Engineering: Identify other important brain measurements or features relevant to specific clinical applications, such as cortical thickness, white matter integrity, or functional connectivity. Develop feature extraction methods or pre-processing steps to incorporate these features into the segmentation model.

Data Augmentation: Expand the training dataset to include a diverse range of brain images with annotations for the additional measurements or features of interest. Augment the data to cover variations in these features and ensure robust model training.

Model Architecture: Modify the model architecture to accommodate the new measurements or features. This may involve adding additional output channels, designing specific modules for feature extraction, or integrating attention mechanisms to focus on different aspects of the brain.

Transfer Learning: Utilize transfer learning techniques to adapt pre-trained models to incorporate the new measurements. Fine-tune the model on a smaller dataset with annotations for the additional features to leverage the knowledge learned from the pre-training phase.

Validation and Interpretation: Validate the model performance on datasets with ground truth annotations for the new measurements. Interpret the model predictions to understand how it utilizes the additional features and assess its clinical relevance and accuracy.

By extending the proposed approach to include other clinically relevant brain measurements or features, the utility of the whole brain segmentation model can be enhanced in various research and clinical settings, enabling a more comprehensive analysis of brain structure and function.